Pre-emptive generation of autonomous unmanned aerial vehicle inspections according to monitored sensor events

ABSTRACT

Methods, systems and apparatus, including computer programs encoded on computer storage media for generation of autonomous unmanned aerial vehicle flight plans based on triggered sensor information. One of the methods includes accessing information correlated from sensors monitoring features of weather events, and determining an upcoming weather event, the determination comprising one or more areas expected to be affected by the weather event. A likelihood of damage associated with the weather event is determined to be greater than a threshold in the areas. The weather event is monitored while areas in which the likelihood is greater than the threshold are updated accordingly. Subsequent to the weather event, properties to be inspected by unmanned aerial vehicles are determined based on severity information associated with the weather event. Job information is generated, the job information being associated with inspecting the determined properties, the job information including jobs each assignable to operators for implementation.

CROSS-REFERENCE TO RELATED APPLICATIONS

Any and all applications for which a foreign or domestic priority claimis identified in the Application Data Sheet as filed with the presentapplication are hereby incorporated by reference in their entirety under37 CFR 1.57.

BACKGROUND

Performing inspections of properties, such as buildings, homes, and soon, after significant weather events (e.g., storms, hail, tornados) caninvolve inspectors traveling to potentially affected properties andperforming a series of time-consuming and complicated steps to fullyinspect each potentially affected property. Generally, inspectors mayneed to perform disparate steps, which may not overlap, to performdifferent types of inspections (e.g., inspecting for hail damage,inspecting for earthquake damage, and so on). Additionally, theseinspections can be preferred to take place rapidly after the significantweather event, for example so that any identified damage can be moreeasily established as being attributable to the weather event.

SUMMARY

Particular embodiments of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages. A system can determine, or monitor, informationindicating upcoming or presently occurring environmental and/or weatherevents, such as storms, hail, tornadoes, rain, wind, earthquakes, and soon. Subsequent to a weather event, the system can trigger unmannedaerial vehicle (UAV) inspections of affected properties (e.g.,properties known to be, or likely to be, affected, such as negativelyaffected and/or damaged). The system can enable the rapid deployment ofUAVs to cover large areas, and optionally in combination with on theground operators, can reduce complexities associated with inspectingproperties. As will be described, the system can pre-emptively generatejobs to be implemented by UAVs, with each job being associated with aninspection of one or more properties. In this way, subsequent to theweather event, the generated jobs can be assigned for implementation to(1) particular UAVs and/or (2) particular operators, thus enabling aninspection company a ‘one-click’ implementation of multitudes ofinspections of properties through automated triggering of the system.

The features described in this specification solve and address numeroustechnical problems associated with inspecting properties for damage. Aswill be described, through use of UAVs, inspections can be completed insignificantly shorter periods of time, and through technical refinementsof computer vision processes, estimations of properties likely to beaffected by particular weather events, real-time information obtainedfrom flying UAVs, and so on, inspections can be assured to be moreaccurate. For example, machine learning systems that utilize computervision can be enhanced through use of images obtained by UAVs astraining data.

Furthermore, unnecessary inspections of properties can be avoided. As anexample, a UAV performing an inspection of properties in a particulararea can determine an extent to which a weather event is affecting theproperties, and the system can limit, or include additional, inspectionsof other areas based on the determined extent. As an example, a UAV mayactively determine that damage is decreasing, or is entirely absent, asthe UAV navigates along a particular direction, and the system candetermine that properties further along the particular direction are notin need of inspection. Thus, the techniques can enable conservation ofresources (e.g., UAV availability), reduce complexities associated withgenerating flight plans, and so on. Additionally, UAVs can include orcarry payload modules, such as sensors of differing types (e.g., visualcameras, infra-red cameras, ultraviolet cameras, distance measuringsensors, and so on), and can perform a multitude of differing types ofinspections at a same time. In this way, a number of UAVs required canbe reduced, thus enhancing efficiency of each inspection.

Furthermore, the system can access information received from specializedhardware, such as sensors or weather devices, located on properties todetermine whether properties are to be inspected. For example, a small,low cost, and limited complexity, specialized piece of hardware may beinstalled on each property and may connect to the system via a network,such as the Internet. The hardware can then monitor weather events suchas pressure, temperature, and so on. The system can additionally accessinformation indicating reduction(s) in efficiencies of solar panels(e.g., solar collectors, particular solar cells, may be damaged during aweather event reducing historical efficiencies), and the system canutilize the accessed information to inform whether particular propertiesare to be inspected. In this way, the system can have access tospecialized sensor information distributed across large areas, and cancorrelate the distributed sensor information to refine estimates of aweather event's course. As an example, a governmental organization mayutilize machine-learning techniques to estimate an area that a weatherevent is likely to affect. The system can increase an accuracyassociated with this estimation based on the specialized sensorinformation.

The system can reduce complexities associated with maintaining andoperating fleets of UAVs, and can limit an extent to which flight plansare required to be manually defined. Thus, the system can reduceprocessing associated with generating flight plans for UAVs. The systemcan further limit errors associated with less sophisticated manual-basedprocesses. Additionally, technical burden can be reduced with respect toensuring people (e.g., employees) are trained to operate differentsoftware and hardware systems which can implement UAV fleet management,flight plan generation, operator assignment, and so on. As describedabove, the system democratizes use of UAVs, for example with respect toinspection purposes, through offloading complex user required actions tobe automatically performed. For example, the system can automaticallymonitor weather events, determine likelihoods of weather eventsaffecting specific properties, optionally generate notifications tousers (e.g., property owners) to determine if they were affected,generate and store job information associated with flight plans, and/orassign job information to operators.

The details of one or more embodiments of the subject matter of thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates an example of a cloud system generating jobinformation in response to an upcoming or present weather event.

FIG. 1B illustrates an example of an unmanned aerial vehicle (UAV)performing an initial inspection of an area.

FIG. 1C illustrates an example of a UAV performing a detailed inspectionof particular properties.

FIG. 2 illustrates a block diagram of a cloud system in communicationwith a user device of an operator.

FIG. 3 illustrates a flowchart of an example process for performinginspections of properties using unmanned aerial vehicles (UAVs).

FIG. 4 illustrates determining properties to be inspected based onfeatures.

FIG. 5 is a flowchart of an example process for generating jobinformation.

FIG. 6 is a flowchart of an example process for updating job informationbased on monitoring a weather event.

FIG. 7 is a flowchart of an example process for providing jobinformation to operators for implementation.

FIG. 8 is a flowchart of an example process for providing flight planinformation to an unmanned aerial vehicle (UAV).

FIG. 9 illustrates an example user interface describing a job to inspecta particular property.

FIG. 10 illustrates a block diagram of an example Unmanned AerialVehicle (UAV) architecture for implementing the features and processesdescribed herein

DETAILED DESCRIPTION

This specification describes a system (e.g., the cloud system 200described below) that can generate jobs to be implemented (e.g.,performed) by unmanned aerial vehicles (UAVs) in response to upcoming orpresently occurring weather events. As will be described, the jobs canbe associated with inspections of properties, such as agriculturalproperties; office, commercial, residential, properties; structures,such as bridges, power transmission towers, cell towers, power plants;and so on. The system can assign the jobs to operators who can travel tolocations proximate to properties that are to be inspected. Incombination with one or more UAVs, the operators can perform theinspections. For example, the system can determine, or receiveinformation indicating, an upcoming hail storm, and can generate jobinformation (e.g., one or more jobs), such that after the hail storm thejobs can be quickly implemented and affected properties inspected.

In this specification, job information includes information associatedwith one or more jobs, with each job specifying one or more of locationinformation, geofence information, type of inspection, UAV information,time restrictions, flight pattern information, and so on. Locationinformation can include global navigation satellite system GNSScoordinates (e.g., property boundary coordinates and/or a centroid of aproperty), address information, longitude/latitude information, and soon. Geofence information can include a geofence boundary which a UAV isto enforce. A geofence boundary can represent an area or volume in whicha UAV is to remain, with the geofence boundary optionally beingdependent or adjustable according to a time of day. A type of inspectioncan include a rooftop inspection, inspection of a structure, inspectionof a power plant, and so on. UAV information can include a type of UAV(e.g., fixed-wing, multi-rotor, land-based, and so on). Timerestrictions can include a window of time during which a job is to beimplemented. Flight pattern information can define a particular flightpattern according to which a UAV is to navigate.

Optionally, a job can specify location information of a property, and aswill be described, a user device of an operator located proximate toproperty can receive the job and determine a particular flight patternfor a UAV to implement. For example, the user device can define pathsconnected by turns, ascents, descents, and so on, which enable the UAVto perform an inspection of the property. The user device can optionallydefine information associated with activating sensors, such as based onthe UAV being at specific waypoints, or activations according todistance or time traveled.

As will be described, the system can receive or access informationassociated with upcoming or presently occurring weather events, and candetermine an upcoming or presently occurring weather event. For example,the system can access information specified by governmental entities(e.g., National Oceanic and Atmospheric Administration, National WeatherService, and so on), information determined from weather maps (e.g.,weather surveillance radar maps), information associated with weathermodels, and so on. As an example, a governmental entity may specify thata hail storm is expected in particular areas (e.g., particular GNSScoordinates, longitude/latitude coordinates), at particular times, withparticular sizes of the hail, and so on. The system can thus monitorinformation received or obtained from the governmental entity, and canidentify the expected hail storm as an upcoming weather event.

Since each weather event may not warrant UAVs inspecting properties, thesystem can determine whether properties are to be inspected. Forexample, hail less than a threshold size may not be known to causedamage (e.g., damage greater than a threshold) or a rainstorm with windless than a threshold may be routine and also not cause damage. In thisexample, for properties location in an area affected by the hail orrainstorm, the system can determine not to inspect the properties. Aswill be described, the system can determine severity informationassociated with a weather event (e.g., hail greater than an inch indiameter may always trigger inspections of affected properties, whilewind less than 20 miles per hour may not trigger inspections), and basedon the severity information can determine whether to generate jobsassociated with inspecting properties. As utilized in thisspecification, severity information includes any information that canaffect or inform a determination as to whether particular properties areto be inspected (e.g., the system can determine a likelihood that aproperty requires inspection based, at least in part, on the severityinformation). The system can monitor historical information indicatingpast weather events and to what extent properties were affected (e.g.,damaged) subsequent to the weather events. Additionally, the system candetermine whether to inspect properties based on other factors,described in more detail with respect to FIG. 4 , including structuralinformation associated with the properties (e.g., clay rooftops maycrack more easily than metal rooftops), and so on.

Optionally, subsequent to the weather event, the system can generatenotifications to be presented to users (e.g., property owners)associated with properties requesting whether the users think that aninspection should be performed. For example, the system can generatenotifications to be presented on user devices (e.g., mobile devices) ofthe users, with the notifications activating one or more electronicapplications (e.g., applications downloaded from an application store),and causing presentation of information received from the system to theusers on the user devices. An example notification may trigger a userinterface to be presented on a user device that includes textualinformation (e.g., “A hail storm was detected last night”), and whichincludes selectable options (“Does your home need to be inspected?[Select yes], [Select no], [Select maybe]”). The system can utilize theresponses to inform a determination of whether to inspect properties.For example, for any affirmative selection by a user, the system candetermine that an inspection is warranted. Additionally, the system candetermine the overall, or measure of central tendency of, receivedresponses. The received responses can inform whether to inspectproperties of users that selected ‘maybe,’ ‘no,’ or that failed torespond. For example, if a threshold number of users selected yes in aparticular area, then the system can determine to inspect otherproperties within a threshold distance.

As will be described, the system can identify properties in need ofinspection, and can generate job information associated with (1) aninitial inspection of one or more areas that include multiple identifiedproperties, and/or (2) detailed inspections of particular identifiedproperties. For example, the system can determine that one or morefixed-wing UAVs are to be deployed, and perform initial inspections oflarge areas. As an example, the system can identify that a weather eventcovered a geographic area encompassing a threshold number of userproperties, that the geographic area was greater than a threshold area,or that the weather event was particularly severe such that propertiesare likely to be affected (e.g., a hail storm had hail greater than athreshold size, such as greater than an inch in diameter). The systemcan therefore generate job information associated with initialinspections by fixed-wing UAVs, and the fixed-wing UAVs can quicklycover large areas (e.g., the fixed-wing UAVs can navigate about an areaat a threshold altitude capturing sensor information, such as images, ofproperties). The system can then analyze received sensor informationfrom the initial inspections, and identify particular properties whichare to receive a more detailed inspection. For example, the system canidentify damage to the particular properties (e.g., based on computervision, machine learning techniques, and so on). As will be described,detailed inspections can utilize multi-rotor UAVs which can obtaindetailed sensor information of individual properties. Performing initialinspections and detailed inspections is described in more detail below,with respect to FIGS. 1A-1C.

Subsequent to generating job information, the system can assignparticular jobs to operators for implementation. For example, the systemcan group jobs associated within a threshold distance of each other(e.g., properties located in a same area), and assign the grouped jobsto a particular operator. The system can access schedule informationassociated with operators, and can automatically determine assignmentsof jobs to operators. Additionally, the system can perform loadbalancing, and based on expected travel time of operators (e.g., travelbetween properties, for example with respect to traffic, distancetraveled, and so on), the system can advantageously group jobs togethersuch that the jobs can be completed in a timely and efficient manner.

Each operator can obtain assigned job information using a user device,also referred to as a ground control system. The operator can thentravel to locations specified in the assigned job information, andperform detailed inspections of properties at the locations. The systemcan optionally monitor progress of the detailed inspections, and basedon the detailed inspections, can determine that additional inspectionsare needed. For example, the system may determine that damage isaffecting more properties than anticipated. In this example, the systemmay determine that properties along one or more directions are damagedand can extend inspections further along the directions. Similarly, thesystem may determine that properties included in an area expanded outfrom an area expected to be affected are damaged, and can extendinspections to properties in the expanded area. The system can alsodetermine that one or more assigned inspections are not needed. Forexample, the system may determine that the damage is less severe thanexpected along one or more directions, and that assigned inspectionsfurther along the directions can be removed.

In this specification unmanned aerial vehicles include drones,un-operated aerial vehicles, remotely operated aircraft, unmannedaircraft systems, any aircraft covered under Circular 328 AN/190classified by the International Civil Aviation Organization, and so on.In addition, certain aspects of the disclosure can be utilized withother types of unmanned vehicles (e.g., wheeled, tracked, and/or watervehicles). Sensors, which are included in the general term payloadmodules (e.g., any hardware, software, module, and so on, that is notcritical to the flight operation of the UAV), can include any devicethat captures real-world information, including cameras, radiationmeasuring instruments, distance detectors such as Lidar, and so on.

FIG. 1A illustrates an example of a cloud system 200 generating jobinformation 4 in response to an upcoming, present, or completed, weatherevent. As described above, a cloud system 200 can identify, ordetermine, that a weather event is predicted to affect a geographicarea, such as a city, neighborhood in a city, particular zip code, andso on. The cloud system can 200 then generate job information 4associated with inspections of properties of users. For example, a usercan be associated with an insurance or inspection company.

As illustrated in FIG. 1A, an example geographic area 10 is illustrated,with the geographic area 10 including properties such as homes,apartment buildings, commercial buildings, park buildings, and so on.Based on weather sensor information 2, the cloud system 200 hasdetermined that a particular portion 20 of the geographic area 10 ispredicted to be affected by a weather event (e.g., a storm, hail,earthquake, wind, tornado, and so on). As will be described in moredetail, the cloud system 200 can monitor information indicating upcomingweather events, such as information obtained from governmental entities,weather services, weather maps, and so on, and can determine that aweather event is expected to affect particular areas (e.g., area 20). Asan example, the cloud system 200 can analyze weather surveillance radarmaps, and optionally through use of weather models, can determine thatweather events are expected to affect particular areas of a geographicarea 10. For example, the cloud system 200 can determine, based on adensity associated with clouds, that hail is expected to affect theportion 20. Additionally, the cloud system 200 can access informationgenerated by governmental entities, and can determine types of weatherevents expected to occur, along with expected locations of the types ofweather events, and information describing a severity of the weatherevents.

The cloud system 200 can determine whether properties of users areincluded in the portion 20 of the geographic area 10 predicted to beaffected by the weather event. The users may be, for example, propertyowners which are insured through an insurance company associated withthe cloud system 200. The cloud system 200 can access property boundaryinformation associated with users located in the geographic area 10, andcan determine whether the property boundary information is included inthe portion 20, or is located within a threshold distance of the portion20. As a time that the weather event is expected to arrive approaches,the portion 20 may increase in size, or may move, and the cloud system200 can therefore anticipate this movement by being, in someimplementations, over-inclusive. Additionally the cloud system 200 canprovide information describing the portion 20 (e.g., boundaryinformation, such as latitude/longitude coordinates, zip code, and soon) to an outside system and can receive indications of users, andlocation information of the users' properties, from the outside system.The outside system may be, for example, a server associated with aninsurance company.

The cloud system 200 can optionally then generate job information 4(e.g., prior to arrival of the predicted weather event), which asdescribed above, can include one or more jobs associated with respectiveinspections of properties of users that are within, or within athreshold distance of, the affected portion 20 of the geographic area10. That is, the cloud system 200 can generate a job associated withinspecting each property in the affected portion 20, and storeinformation associated with job. In this way, subsequent to theoccurrence of the weather event, the job can be implemented (e.g., anunmanned aerial vehicle (UAV) can perform an inspection of an associatedproperty). As will be described, the job information 4 can be modifiedover time, for example as the weather event proceeds closer to a time ofarrival, and/or subsequent to the occurrence of the weather event. Inthis way, the cloud system 200 can reduce a number of properties thatare to be inspected, or increase a number of properties, based on themovement, severity, and so on, of the weather event.

Optionally, the cloud system 200 can determine a likelihood of theweather event being associated with damage to properties, and cangenerate job information 4 upon the likelihood exceeding a threshold.The cloud system 200 can therefore filter weather events from triggeringthe generation of job information 4, thus reducing unnecessaryprocessing time and decreasing storage space associated with maintainingjob information 4. Optionally, the threshold can depend on a volatilityassociated with a type of the weather event, for example the cloudsystem 200 may store, or may have determined based on historical weatherevents (e.g., through use of machine learning models), that a first typeof weather event (e.g., a hail storm) can be harder to predict than asecond type of weather event (e.g., wind). As an example, the cloudsystem 200 may therefore determine to generate job information 4 basedon a predicted size (e.g., diameter) of hail being relatively low, asproperties may be damaged by hail occasionally being larger than thepredicted low size. Additionally, the cloud system 200 may monitor andupdate thresholds based on empirically determined information, or mayupdate determinations of likelihoods. For example, the cloud system 200may determine that a particular weather event, such as wind speeds thatexceed a threshold, are more likely to cause damage than initiallydetermined (e.g., based on machine learning models, information obtainedfrom governmental entities indicating severities of weather events, andso on), and can increase the likelihood. Additionally, the cloud system200 can update determinations of likelihoods for specific areas,specific properties, specific materials used in properties, and so on.For example, the cloud system 200 may determine that for a particulararea, a first threshold wind speed (e.g., 40 miles per hour) generallydoes not require inspections of properties, while for a different areathe first threshold wind speed does require inspections. Similarly, ifthe cloud system 200 can access information (e.g., from outside systems,such as systems associated with insurance companies, real estatecompanies, governmental databases) indicating types of materials used inconstruction of properties, the cloud system 200 can determine that thefirst threshold wind speed is not likely to negatively affect brickproperties, while it is likely to affect wooden properties. If thelikelihood is less than the threshold, the cloud system 200 may insteaddetermine not to generate job information 4. The system 200 may, forexample, generate job information 4 subsequent to the weather event forany users that request an inspection be performed, or optionally thecloud system 200 can provide notifications to users requesting whetherthey need an inspection.

As described above, the cloud system 200 can generate job information 4prior to the occurrence of the weather event in the portion 20 of thegeographic area 10. Optionally, the cloud system 200 can insteadgenerate job information 4 subsequent to the occurrence of the weatherevent. For example, the cloud system 200 can (1) determine that aweather event is predicted to affect the portion 20, (2) determine,subsequent to the weather event, properties included in the portion 20that are to be inspected, and/or (3) generate job information 4 for thedetermined properties. For example, the cloud system 200 can initiallystore information associated with the weather event, including anindication of the portion 20, severity information associated with theweather event, and so on. Subsequent to the weather event, the cloudsystem 200 can determine whether specific properties of users are to beinspected, and for each specific property, can generate a job associatedwith the inspection.

As an example, the cloud system 200 can obtain information identifyingan actual portion 20 that was affected by the weather event, and cangenerate jobs for properties included in the actual portion. Forexample, as described below with respect to FIG. 1B, an initialinspection by a fixed-wing UAV can be performed, and properties that aredamaged, or that are likely to be damaged, can have jobs generated formore detailed inspections. Additionally, the cloud system can generateautomatic notifications to users of properties located within athreshold distance of the portion 20, and based on the responses cangenerate jobs (e.g., the cloud system 200 can automatically call mobiledevices of users, such as a robo-call, and request a response). As anexample of automatic calls, questions indicative of likelihoods ofdamage can be automatically posed to users (e.g., prepared remarks, orremarks dynamically generated based on the weather event, can beprovided, such as “did you experience loud wind last night,” “did youexperience loud hail last night,” and so on). Furthermore, the automaticnotifications can be provided via an electronic application executing onthe user's mobile devices (e.g., an application associated with aninsurance company can prompt the users to enter information).

As described above, an initial inspection of the portion 20 canoptionally be performed by one or more fixed-wing UAVs subsequent to theweather event. The initial inspection can be utilized to inform specificproperties in the portion 20 that are to receive a detailed inspection.

FIG. 1B illustrates an example of an unmanned aerial vehicle (UAV) 30performing an initial inspection of an area 20 subsequent to a weatherevent. As described above, the cloud system 200 can generate jobinformation 4 associated with an initial inspection of the area 20subsequent to the weather event. In this way, the cloud system 200 canreceive sensor information across the entirety of the area, and candetermine (e.g., based on computer vision techniques, optionally incombination with reviewers viewing the sensor information) specificproperties included in the area 20 that are to be inspected in moredetail (e.g., described below, with respect to FIG. 1C).

The initial inspection can be performed such that the UAV 30 (e.g., afixed-wing UAV, optionally multiple fixed-wing UAVs assigned todifferent portions of the area 20 or multiple areas affected by weatherevents) can obtain sensor information sufficiently detailed to inform adetermination of whether properties are to be inspected in more detail.For example, the UAV 30 can navigate at a particular altitude, and atless than a threshold speed, such that obtained sensor information(e.g., images) include at least a particular level of detail (e.g., thecloud system 200, or an operator associated with the UAV 30, can specifya minimum level of detail, such as a minimum number of pixels perdistance or pixels per distance²). The cloud system 200, or optionally auser device of the operator, can generate a flight pattern that causesthe UAV 30 to navigate (e.g., autonomously navigate) about the area 20,and obtain sensor information describing the entirety of the area 20, oroptionally describing at least all user properties included in the area20. As will be described, for example with respect to FIG. 4 , thedetermination of which properties are to be inspected in more detail canbe based on the obtained sensor information. Optionally, thedetermination can be based on additional information such as weathersensors included on users' properties, and/or severity information(e.g., reports obtained from governmental entities, news reports,requests for inspections from users, and so on).

The cloud system 200 can trigger the initial inspection based oninformation associated with the weather event. For example, a first typeof weather event (e.g., a hailstorm, or a hail storm with hail less thana threshold diameter) may not benefit from the initial inspection as theexpected damage may not be clearly visible in obtained sensorinformation, while a second type of weather event (e.g., a tornado, windwith speeds greater than a threshold, such as 70, 80, 90, miles perhour) may benefit from the initial inspection. Additionally, the cloudsystem 200 can utilize expected, or determined, severities of weatherevents to trigger the initial inspection. For example, the cloud system200 can trigger the initial inspection upon wind speeds being greaterthan a first threshold (e.g., 70, 80, miles per hour). As anotherexample, for wind speeds less than the first threshold the cloud system200 can request that users indicate whether their properties are to beinspected or utilize other features to determine properties (e.g.,described in more detail below, with respect to FIG. 4 ).

Subsequent to the initial inspection, the cloud system 200 can analyzeobtained sensor information (e.g., images) obtained during the initialinspection, and can identify specific user properties that have visibledamage. The cloud system 200 can also identify user properties that haveindications of potential damage. The cloud system 200 can also identifyuser properties that are unclear as to whether damage exists. The cloudsystem 200 can also identify user properties that have no damage. Forexample, the cloud system 200 can utilize computer vision techniques tocompare previously taken images of properties, with images obtainedduring the initial inspection, and can determine based on comparisonswhether damage is evident in the images. Additionally, the cloud system200 can utilize scale invariant feature transform techniques, eigenspacetechniques, techniques to determine outlines included in images, andmatch outlines to shapes of damage or features associated with damage,and so on.

Optionally, the cloud system 200 can present the obtained sensorinformation to one or more reviewing users, and the reviewing users canspecify properties that are to be inspected in more detail. For example,the cloud system 200 can generate user interface to be presented via oneor more displays (e.g., displays of user devices in communication withthe system 200), and can include the sensor information for review. Thereviewing users can then interact with the user interface to zoom in,zoom out, pan, notate portions, cause highlighting of propertiesassociated with users, request any prior historical images of properties(e.g., obtained during prior initial inspections or detailedinspections), and so on. The reviewing users can then assign whetherindividual properties are to be inspected. For example, the reviewingusers can assign that a particular property (1) has evident damage, (2)has indications of damage, or (3) has no damage. Upon an assignment, theuser interface can optionally shade the property according to theassignment (e.g., adjust a color of the property as illustrated). Forexample, an assignment of damage can cause a property to be shaded afirst color (e.g., green, yellow, red). As another example, anassignment of indications of damage can cause the property to be shadeda second color (e.g., green, yellow, red). As another example, anassignment of no damage can cause the property to be shaded a thirdcolor (e.g., green, yellow, red). In this way, subsequent reviewingusers can quickly view the user interface and determine which propertiesare in need of inspection, and can analyze outliers closely (e.g., a redproperty surrounded by green properties), and so on. Additionally, thecloud system 200 can perform particular types of computer visionprocessing techniques to aid the reviewing users. For example, the cloudsystem 200 can adjust a contrast of the images to highlight features,perform boundary detection, and so on.

The cloud system 200 can further generate rectified images which can bepresented to the reviewing user. A rectified image can be, for example ageo-rectified images, such as an ortho-mosaic of geo-rectified imagesstitched together from the obtained sensor information (e.g., images, orother sensor information). The rectified image can be generated based ona GNSS receiver included in the UAV 30, optionally in combination withpose information associated with the sensor information (e.g., camerapose). In this way, when reviewing users select a particular property(e.g., select a portion of a rectified image), and assign the particularproperty to be inspected, the cloud system 200 can store locationinformation associated with the particular property. As will bedescribed below, each of the properties to be inspected can beassociated with a waypoint, indicating that a more detailed inspectionis to take place (e.g., using a multi-rotor UAV).

FIG. 1C illustrates an example of an unmanned aerial vehicle (UAV) 40performing a detailed inspection of particular properties. The cloudsystem 200 can determine particular properties that are to receivedetailed inspections, and can generate jobs associated with theinspections. As will be described, one or more operators can be assigneda portion, or all, of the jobs, and can travel to the particularproperties with the UAV 40 (e.g., a multi-rotor UAV). The UAV 40 canthen navigate according to a flight plan (e.g., generated by the cloudsystem, or optionally generated by a user device of the operator), whichcan depend on a type of the property or type of damage expected. Forexample, inspecting a cell-phone tower may be associated with aparticular flight pattern in which the UAV 40 navigates at a particularaltitude to waypoints surrounding the tower. In this example, at eachwaypoint the UAV 40 can descend to within a threshold distance of theground obtaining sensor information, and after ascending back to thewaypoint can navigate to a subsequent waypoint. As another example,inspecting a rooftop of a residential home can be associated with adifferent flight pattern. In this example, the UAV 40 can navigate at asame distance from the rooftop obtaining sensor information.

As illustrated in FIG. 1C, each of the particular properties isassociated with a waypoint (e.g., waypoints 42A-42P), and each job canindicate one or more of the waypoints. For example, each waypoint canspecify a centroid of the property, and a particular flight pattern toinspect the property can be determined based on the waypoint. As anexample, the cloud system 200, or optionally the user device of theoperator, can access property boundary information associated with theproperty, and can generate the flight pattern to cause the UAV 40 toobtain sensor information describing the property. Additionally, thecloud system 200, or user device, can determine a portion of theproperty that is to be inspected (e.g., a rooftop). For example, thedetermination can be based on images obtained during the initialinspection (e.g., the cloud system 200 or user device can identifyboundary information of a rooftop). Determining a flight pattern isdescribed in more detail below.

Furthermore, the UAV 40 can utilize disparate sensors to sufficientlyobtain a detailed inspection. The UAV 40 can be required to obtainimages with at least a threshold level of detail (e.g., as describedabove, a minimum number of pixels per distance), and based on theweather event, can utilize sensors that are useful in indicating damage.For example, subsequent to a hail storm the UAV 40 can utilize heatsensors to determine portions of a rooftop that are leaking heat, whichas an example may indicate damage.

In addition to generating job information 4, the cloud system 200 canassign included jobs to particular operators. For example, the cloudsystem may assign jobs associated with waypoints 42A-42F to a firstoperator, while a second operator may be assigned the remainingwaypoints. To determine which jobs are assigned to which operators, thecloud system 200 can access schedule information indicating availableoperators, and can group jobs together according to geographic area,optionally in combination with expected times to navigate to eachwaypoint included in a group. For example, jobs proximate to each other,but included in a dense city with high traffic and/or one-way streets,may be separated into distinct groups.

FIG. 2 illustrates a block diagram of a cloud system 200 incommunication with a user device 220 of an operator 240. The cloudsystem 200 can be a system of one or more computers, or one or morevirtual machines executing on a system of one or more computers, and canmaintain, or be in communication with, one or more databases or storagesubsystems (e.g., databases 206, 208, 209). The user device 220 can be alaptop, tablet, mobile device, wearable device, and so on, that isassociated with an operator 240 implementing inspections in combinationwith one or more unmanned aerial vehicles (UAVs 230A-230N). The userdevice 220 can be in communication (e.g., wired or wirelesscommunication) with the cloud system 200, for example over a cellularconnection (e.g., 3G, 4G, LTE, 5G), or via a mobile device of anoperator. As an example, the mobile device can be in communication withthe cloud system 200 over a cellular connection, and the mobile devicecan relay information over a Bluetooth or Wi-Fi connection between theuser device 220 and cloud system 200. Similarly, the UAVs (e.g., UAV240A) can receive information from, and provide information to, the userdevice 220, for example over a wired or wireless communication.

As described above, the cloud system 220 can monitor informationassociated with upcoming or presently occurring weather events (e.g.,weather information 201), and can generate job information 210associated with inspections of particular properties. The cloud system220 includes a weather event determination engine 204 that can accessweather information 201 obtained from governmental entities. Forexample, the engine 204 can monitor web pages, XML data, and so on,associated with governmental entities that specify weather events. Theweather event determination 204 can access information associated withweather models, sensor information from specialized sensors installed onproperties, and so on, and can determine, or detect (e.g., via weathersensors), upcoming or presently occurring weather events. As describedabove, the weather events can be analyzed, such that weather eventswhich are unlikely to cause damage may be filtered, while weather eventswith a likelihood of damage being greater than a threshold, may triggergeneration of job information 210. Determining likelihoods of weatherevents being associated with damage is described in more detail below,with respect to FIG. 3 .

Based on determining that a likelihood of damage being caused by aweather event exceeds a threshold, the weather event determinationengine 204 can determine properties associated with users that areincluded in an area expected to be affected by the weather event. Asdescribed above, a user of a property can indicate a property insured byan insurance company associated with the cloud system 200. The weatherevent determination engine 204 can access one or more databases (e.g.,the user/property database 209) and can identify properties included, orwithin a threshold distance of, the area expected to be affected. Theengine 204 can store the identifications (e.g., in the job informationdatabase 206), and can optionally update the identifications to remove,or add additional, properties as the weather event progresses orfinishes.

The cloud system 200 further includes a job determination engine 202that can generate job information 210, and assign the job information210 to particular operators (e.g., operator 240) for implementation. Forexample, and as described above, the cloud system 200 can obtainoperator information, such as schedule information in an operatordatabase 208, and can assign particular jobs to operators. As describedabove, the cloud system 200 can determine that an initial inspection isto be performed (e.g., via a fixed-wing unmanned aerial vehicle, forexample based on a type of weather event, severity of the weather event,and so on), and/or that detailed inspections of particular propertiesare to be performed. The job determination engine 202 can generate thejob information, which can include jobs associated with respectiveproperties and can indicate one or more of location informationassociated with a property, geofence boundary information, times duringwhich the job can be implemented, and so on.

The user device 220 of the operator 240 can execute an application(e.g., an application obtained from an electronic application store, asoftware application installed on the user device 220), and theapplication can generate user interface 226 data for presentation to theoperator 240. As will be described below, and illustrated in FIG. 9 ,the user device 220 can present user interfaces associated with each jobreceived by the user device (e.g., assigned to the operator 240 by thecloud system 200), for example representations of a flight patternassociated with inspecting a property, geofence boundary information,and so on. Optionally the operator 240 can modify aspects of the flightpattern, such as a launch/landing location, particular waypoints to betraveled to by a UAV, actions to take at each waypoint or betweenwaypoints (e.g., activating particular sensors), and so on. Optionallythe operator 240 may modify particular aspects of a flight pattern, butbe restricted with respect to other aspects (e.g., the operator 240 canmodify a flight pattern, but be constrained by a geofence boundaryassociated with a property boundary of a property being inspected).

The user device 220 can provide flight plans 230A (e.g., informationsufficient to enable a UAV to navigate, for example autonomouslynavigate, and implement an inspection of one or more properties) to oneor more UAVs 230A-230N that are to implement the flight plans 230A.Subsequent to, and/or during, implementation of a flight plan, the userdevice 220 can receive inspection information 232 from a UAV, such assensor information (e.g., camera images, heat information, radiationinformation, pressure information, distance readings, and so on). Thisinspection information 232 can be provided to the cloud system 200 forstorage, and further processing (e.g., damage can be identified usingcomputer vision techniques, optionally in concert with reviewing users).

FIG. 3 illustrates a flowchart of an example process 300 for performinginspections of properties using unmanned aerial vehicles (UAVs). Forconvenience, the process 300 will be described as being performed by asystem of one or more computers (e.g., the cloud system 200).

One or more features described in process 300 can be triggered to beperformed (e.g., by the system) automatically (e.g., without userinput), while one or more other features can be performed in response touser input. A user of the system (e.g., an administrator) can specifyactions and features that are to be performed automatically, for examplein response to determined weather events, and other actions and featuresthat are to be performed in response to user input.

As an example, the system can determine upcoming, or presentlyoccurring, weather events through routine (e.g., periodic) monitoring ofweather information, or the system can subscribe to updates regardingweather events (e.g., subscribe to updates received from governmentalentities), and so on. In response to determining upcoming or presentlyoccurring weather events, the system may automatically determine userproperties in one or more areas that may be affected by the weatherevent, along with information indicating likelihoods of damage to theuser properties. A user of the system may then review the determineduser property information, and provide user input to the systemindicating that the system is to generate job information. The jobinformation can include one or more jobs associated with fixed-wing UAVinspections of the one or more areas. The job information can alsoinclude jobs associated with detailed inspections of the determined userproperties. That is, the user may view a user interface (e.g., generatedby the system, or that can provide information to, and receiveinformation from, the system), or set rules for the system to follow(e.g., rules can specify conditions for which user input is required,such as specific types of weather events may require user input), andthe user can rapidly indicate whether to proceed with generating jobinformation, assigning the job information to operators forimplementation, and so on. Alternatively, the system may automaticallygenerate job information. For example, if a likelihood of damage of adetermined weather event exceeds a threshold (e.g., a hail storm withgreater than a threshold size hail, a storm with greater than athreshold wind speed), the system may automatically generate jobinformation associated with a fixed-wing UAV inspection, detailedinspections of properties, and so on. In this way, the system may reducecomplexities associated with monitoring weather events, determiningproperties likely to be affected by the monitored weather events,preparing flight plans for individual inspections of properties, and soon. Causing inspections of properties, including initial high-levelfixed-wing UAV inspections, can advantageously be reduced in complexityto a ‘one-click.’ That is a user of the system may simply select, on auser interface, that after a weather event, the system proceed withgenerating a job associated with an initial inspection. For example, thesystem can prepare a flight plan for a fixed-wing UAV to autonomouslyfollow, designate geofence boundaries associated with an area ofinspection, and assign the job to an operator.

The system determines an upcoming weather event (block 302). Asdescribed above, the system can monitor information obtained from, andgenerated by, governmental entities, weather entities (e.g., weatherprediction companies, agencies), weather sensors, and so on, and candetermine the existence of the upcoming weather event. For example, agovernmental entity can publish upcoming weather events such as hailstorms, and the published information can indicate a location (e.g., ageographic area, a zip code, a city name, a neighborhood name, boundaryinformation) that the weather event is expected to affect. The publishedinformation can further indicate a type of the weather event (e.g., hailstorm). The published information can further indicate an expectedseverity of the weather event (e.g., an expected size of hail, or for astorm, expected rainfall, wind speed, tornado information and ratings,and so on).

The system can filter weather events based on a likelihood of theweather event causing damage being below a threshold. To determine thelikelihood, the system can utilize machine learning models trained onhistorical information associated with weather events and damageexperienced from the weather events. As an example, a governmentalentity may publish prior weather events describing an expected damage ofthe weather event, along with information describing actual damage. Theinformation escribing actual damage may textually describe damageexperienced (e.g., ‘5 homes were damaged,’ ‘damage reported in officebuilding,’ ‘windows broken,’ ‘hail greater than 1 inch in diameterreported,’ and so on), and the system may parse the textual data todetermine information indicating an extent of the damage. Additionally,the machine learning models can store general rules associated withweather events and likelihoods of damage. For example, the system candetermine, or store information identifying, that tornados withparticular ratings (e.g., Fujita scale ratings) are associated withparticular likelihoods of damage, or hail of differing sizes are eachassociated with differing likelihoods of damage, and so on.Additionally, the system can learn likelihoods of damage over time, forexample after weather events the system can determine a number of claimsactually filed for damage of properties, and the extent of the damageidentified, and can update the likelihoods.

The system determines properties to be inspected (block 304). Subsequentto determining a weather event (e.g., an upcoming or presently occurringweather event), the system determines particular properties of usersthat may be affected by the weather event (e.g., negatively affected,such as that a likelihood of the property being damaged exceeds athreshold). The system can update the determination as the weather eventprogresses, or completes, as will be described below with respect toFIG. 4 , and can obtain properties that are to be inspected. Since theremay be multitudes of properties in areas expected to be affected by theweather event, but only particular properties associated with users(e.g., users whose properties are insured by a company associated withthe system), the system can either (1) obtain information identifyinguser properties and filter the user properties to properties located inareas expected to be affected, or (2) provide information describing theareas expected to be affected to an outside system. The outside may be asystem associated with an insurance company, inspection company, and soon. The system can then receive information indicating locations of userproperties within the affected areas.

The system generates job information associated with inspectingproperties (block 306). As described above, the system can generate jobsassociated with inspecting the properties determined in block 304. Forexample, the system may rapidly deploy one or more fixed-wing UAVs toperform an initial inspection, and the system, optionally in combinationwith a reviewing user as described above, may determine properties thatare to be inspected in more detail. Through generating jobs, and as willbe described assigning the jobs to operators, all of the determinedproperties may be quickly designated as needing inspections.

The system provides job information to operators for implementation(block 308). As described above, and as will be described in more detailbelow with respect to FIG. 7 , the system assigns jobs to operators. Thesystem can access schedule information associated with operators, alongwith availability of UAVs, or UAVs that include particular sensors(e.g., after a hailstorm, the system may reserve UAVs that include orcan access heat sensors), and can determine a number of availableoperators and/or UAVs that can implement the jobs. The system can thenassign jobs to operators, which can optionally be grouped according togeographic locations of the jobs (e.g., the system can clusterproperties that need to be inspected into jobs assigned to a sameoperator).

An assignment of one or more jobs to an operator can include the systemproviding (e.g., pushing) information a user device of an operator(e.g., a user device from which the operator logs into the system, oraccesses the system) describing the jobs. Additionally, the assignmentcan specify times during which the operator is to complete the jobs,along with location information associated with each job. Optionally atype of inspection to be performed can be presented. As an example withrespect to a rooftop damage inspection, the user device can presentinformation indicating that “damage was identified on the rooftop ofthis property. As illustrated in FIG. 9 , the user device can presentuser interfaces describing each job, for example one or more images of aproperty associated with the job, along with a flight pattern associatedwith inspecting the property (e.g., determined by the user device basedon the location information, type of inspection, and so on, ordetermined by the system and provided to the user device). The userdevice can also provide a flight plan to a UAV while located proximateto the property with the flight plan, thus enabling the UAV to performthe inspection (e.g., autonomously perform the inspection).

FIG. 4 illustrates determining properties to be inspected based onfeatures (e.g., features 402-412). For convenience, determiningproperties will be described as being performed by a system of one ormore computers (e.g., the cloud system 200). As described above withrespect to FIG. 3 , the system determines properties to be inspectedsubsequent to determining that a weather event is to affect one or moreareas. The features 402-412 illustrated in FIG. 4 can be utilized toinform the determination of properties, and can be combined, weighted,and so on, to determine actual properties to be specified in jobsassigned to operators.

To determine particular properties for inspection, the system canmonitor a progress of the weather event 402, for example monitor areasexpected to be affected by the weather event, and identify userproperties located in the monitored areas. That is, a weather event mayinitially be designated (e.g., by a governmental entity) as affecting alarge area, however as the weather event progresses the designation canbe updated to reduce the area, and as the weather event occurs the areacan be reduced or eliminated. The system can therefore monitor an areathat is actively expanding or reducing in size, until subsequent to theweather event.

Additionally, the system can generate notifications 404 to be providedto users located in areas expected to be affected, for example asdetermined in 402, and the notifications can be presented on userdevices of users as described above. That is, subsequent to the weatherevent, the system can request that users located within, or proximateto, the determined areas indicate whether their property is in need ofinspection (e.g., the user device can request whether hail was heard,loudness of wind, whether damage is evident, and so on. Additionally,and as described above, the system can automatically call users 406, orprovide SMS, MMS, texts or notifications to users, requestinginformation.

The system can access specialized hardware 408 included on one moreproperties, which can measure weather information via one or moresensors, and can utilize the information to inform a more detailedpicture of the weather event. For example, an insurance company mayrequire that users include specialized hardware on their rooftops, orthat a threshold number of users include the hardware within aparticular area, and the specialized hardware can communicate with thesystem, or with an outside system that provides information to thesystem. As an example, the hardware may connect via the Internet, orthrough mobile connections, to the system, or outside system, and canprovide sensor information measured during the weather event. The sensorinformation can include temperature, pressure, images, and so on, andthe system can determine properties to be inspected based on thespecialized hardware.

The system can determine severity information associated with theweather event 410. As described above, the system can determine alikelihood of damage, along with an area in which the likelihood isgreater than a threshold. For example, the system can monitor the areaexpected to be affected by the weather event, and based on informationreceived from governmental entities, or via analyzing weather radarimages and feeds, the system can determine likelihoods of damage.

Subsequent to the weather event, the system can access informationindicative of damage to properties, for example reductions inefficiencies of solar cells 412 installed on the properties. Since areduction in efficiency can indicate damage to the solar cells, thesystem can utilize the information to inform whether the property may bedamaged. For particular types of weather events, such as hail, tornados,earthquakes, and so on, reductions in efficiencies can be more stronglycorrelated to actual damage, while other types of weather events, suchas blizzards, may indicate that the solar cells are being blocked (e.g.,snow has collected on them). The system can determine an expectedefficiency given sunlight conditions, average or actual placement of thesolar cells on properties, and so on, to determine whether reductionsare being experienced.

As described above, an initial fixed-wing UAV inspection 414 can beperformed, and the system can determine an extent of damage toproperties based on obtained sensor information. A reviewing user candesignate properties that are to be inspected, or properties that mayneed to be inspected, and the system can incorporate the informationinto the above factors to generate a more complete picture of propertiesnegatively affected.

Based on the factors 402-414, the system can accurate determineproperties that are to be inspected. For example, the system can weight(e.g., the system can store information indicating weights) each of thefactors, and determine properties that satisfy a threshold (e.g., thesystem can determine a value for each factor, for each property, andafter weighting can determine an overall score associated with thefactors). Optionally, if the system determines that particularproperties are to be inspected, but one or more other properties withina threshold distance are not to be inspected, the system can assign jobsfor the other properties, or optionally indicate that a UAV inspecting aproximate property is to point its sensors (e.g., cameras) towards theother properties, and actively determine whether the other propertiesare to be inspected. In this way, if the system isn't sure (e.g.,greater than a threshold) that the other properties need inspection, thesystem can obtain sensor information (e.g., images) of the propertieswhile a UAV is inspecting a proximate property. Furthermore, if thedamage to the proximate properties is greater than a threshold (e.g.,greater than expected), the system can determine that the otherproperties are likely to also be damaged.

FIG. 5 is a flowchart of an example process 500 for generating jobinformation. For convenience, the process 500 will be described as beingperformed by a system of one or more computers (e.g., the cloud system200).

The system receives information indicating properties to be inspected(block 502). As described above, with respect to FIGS. 3-4 , the systemcan determine properties that are to be inspected.

The system optionally generates job information for a fixed-winginspection of one or more areas that include the indicated properties(block 504). As described above, the system can determine propertiesthat are to be inspected, and the system can perform an initialinspection to update the determination. In this way, the system canremove initially determined properties if they are not in need of aninspection, can expand to additional properties, and so on.

The system generates job information for detailed inspections ofparticular properties (block 506). As described above, the system cangenerate a job for each property that is to be inspected, or canoptionally combine adjacent properties into a same job (e.g., a geofenceboundary can include both properties, and a UAV can inspect a firstproperty, then navigate immediately to the second property).

FIG. 6 is a flowchart of an example process 600 for updating jobinformation based on monitoring a weather event. For convenience, theprocess 600 will be described as being performed by a system of one ormore computers (e.g., the cloud system 200).

As described above, with respect to FIG. 3 , the system determines anupcoming weather event (block 602), and determines properties to beinspected (block 604). Optionally, the system can pre-emptively generatejob information (block 606) associated with the determined properties.As the weather event progresses, the system can update its determinationof properties to be inspected through monitoring the weather event(block 608), and can generate updated job information (block 610) basedon the occurrence of the weather event. As described above, throughpre-emptively generating job information, the system can immediatelyready to perform inspections. Alternatively, the system can generate jobinformation subsequent to the weather event associated with particularproperties, for example as determined in FIG. 4 .

FIG. 7 is a flowchart 700 of an example process for providing jobinformation to operators for implementation. For convenience, theprocess 700 will be described as being performed by a system of one ormore computers (e.g., the cloud system).

The system access schedule information of operators (block 702). Asdescribed above the system can store, or maintain, schedule informationassociated with operators, and identify operators that are available toperform inspection of properties.

The system designates waypoints associated with job information (block704). As illustrated in FIG. 1C, the system designates waypoints (e.g.,a centroid of a property) for each job, and can therefore determineroutes connecting the waypoints. The system determines operators to beassigned to particular jobs based on the waypoint information, androutes connecting the waypoints (block 706). The system can cluster jobssuch that a route connecting waypoints associated with the cluster jobsare within a threshold distance, or along a same direction, that loopfrom a starting position back to within a threshold distance of thestarting position, and so on. The system can access traffic information,such as expected traffic information, and can utilize load balancingtechniques to determine a total number of operators. The system candetermine an assignment of the operators to particular jobs. The systemcan determine an assignment of UAVs to operators. For example, thesystem may have a threshold number of UAVs that can be used to inspectthe properties (e.g., the inspection may require a heat sensor), and thesystem can assign time slots for use of the UAVs to operators, andassign routes such that a maximum number of properties can be inspectedin a day.

The system provides job information to operators (block 708). Asdescribed above, the system assigns jobs to operators, and user devicesof the operators can receive, or obtain, job information associated withthe inspections. Optionally, the system can provide any updates to jobinformation to user devices, for example via a mobile network (e.g., 4G,3G, LTE). As an example, the system can determine that inspections ofproperties are no longer needed, for example based on receivinginformation from inspections indicating that damage is not as extensiveas expected. The system can then automatically remove jobs, notifyoperators regarding the removal, and so on.

FIG. 8 is a flowchart of an example process 800 for providing flightplan information to an unmanned aerial vehicle (UAV). For convenience,the process 800 will be described as being performed by a user device ofone or more processors (e.g., the user device 220).

The user device receives job information assigned to an operator of theuser device (block 802), and the user device optionally provides userinterface data describing one or more jobs (block 804). For example, theuser device can present a map of an area that includes waypointsassociate with each assigned job. Additionally, the user device canreceive input (e.g., a pinch-to-zoom), and can present detailedinformation associated with particular jobs. For example, the operatorcan select (e.g., double tap, tap with greater than a threshold force orpressure, zoom in on) a particular waypoint, and the user device canpresent the detailed information. Detailed information can include anoverview of a flight plan, such as waypoints that a UAV is to follow(e.g., waypoints from a corner of a rooftop to an opposite corner,including actions to be taken at waypoints or between waypoints),geofence boundary (e.g., property boundary), and so on. The user devicecan optionally receive modifications to a flight plan (block 806), forexample as described above the operator can modify, or set, astarting/launch location, change waypoints, indicate obstacles that theUAV is to avoid, and so on. The user device can then provide a flightplan to the UAV for implementation (block 808). The UAV can thenimplement the flight plan, for example according to the UAV architecturedescribed below.

FIG. 9 illustrates an example user interface 900 describing a job toinspect a particular property. As illustrated, the user interface 900includes a representation of the property (e.g., house 902), along witha geofence boundary (e.g., boundary connected by 904A-904D), flightpattern (e.g., flight pattern over rooftop 906), launch/landinglocations (e.g., location 910), and so on. An operator utilizing theuser interface 900 can select layers 908 to be presented in the userinterface 900, such as solely presenting the geofence boundary,inspection area (e.g., rooftop 906), and so on.

FIG. 10 illustrates a block diagram of an example Unmanned AerialVehicle (UAV) architecture for implementing the features and processesdescribed herein. A UAV primary processing system 1000 can be a systemof one or more computers, or software executing on a system of one ormore computers, which is in communication with, or maintains, one ormore databases. The UAV primary processing system 1000 can be a systemof one or more processors 1035, graphics processors 1036, I/O subsystem1034, logic circuits, analog circuits, associated volatile and/ornon-volatile memory, associated input/output data ports, power ports,etc., and/or one or more software processing executing one or moreprocessors or computers. Memory 1018 may include non-volatile memory,such as one or more magnetic disk storage devices, solid state harddrives, or flash memory. Other volatile memory such a RAM, DRAM, SRAMmay be used for temporary storage of data while the UAV is operational.Databases may store information describing UAV flight operations, flightplans, contingency events, geofence information, component information,and other information.

The UAV processing system may be coupled to one or more sensors, such asGPS receivers 1050, gyroscopes 1056, accelerometers 1058, pressuresensors (static or differential) 1052, current sensors, voltage sensors,magnetometer, hydrometer, and motor sensors. The UAV may use an inertialmeasurement unit (IMU) 1032 for use in navigation of the UAV. Sensorscan be coupled to the processing system, or to controller boards coupledto the UAV processing system. One or more communication buses, such as aCAN bus, or signal lines, may couple the various sensor and components.

Various sensors, devices, firmware and other systems may beinterconnected to support multiple functions and operations of the UAV.For example, the UAV primary processing system 1000 may use varioussensors to determine the vehicle's current geo-spatial location,attitude, altitude, velocity, direction, pitch, roll, yaw and/orairspeed and to pilot the vehicle along a specified route and/or to aspecified location and/or to control the vehicle's attitude, velocity,altitude, and/or airspeed (optionally even when not navigating thevehicle along a specific path or to a specific location).

The flight control module 1022 handles flight control operations of theUAV. The module interacts with one or more controllers 1040 that controloperation of motors 1042 and/or actuators 1044. For example, the motorsmay be used for rotation of propellers, and the actuators may be usedfor flight surface control such as ailerons, rudders, flaps, landinggear, and parachute deployment.

The contingency module 1024 monitors and handles contingency events. Forexample, the contingency module may detect that the UAV has crossed aborder of a geofence, and then instruct the flight control module toreturn to a predetermined landing location. Other contingency criteriamay be the detection of a low battery or fuel state, or malfunctioningof an onboard sensor, motor, or a deviation from the flight plan. Theforegoing is not meant to be limiting, as other contingency events maybe detected. In some instances, if equipped on the UAV, a parachute maybe deployed if the motors or actuators fail.

The mission module 1029 processes the flight plan, waypoints, and otherassociated information with the flight plan as provided to the UAV inthe flight package. The mission module 1029 works in conjunction withthe flight control module. For example, the mission module may sendinformation concerning the flight plan to the flight control module, forexample lat/long waypoints, altitude, flight velocity, so that theflight control module can autopilot the UAV.

The UAV may have various devices connected to it for data collection.For example, photographic camera 1049, video cameras, infra-red camera,multispectral camera, and Lidar, radio transceiver, sonar, TCAS (trafficcollision avoidance system). Data collected by the devices may be storedon the device collecting the data, or the data may be stored onnon-volatile memory 1018 of the UAV processing system 1000.

The UAV processing system 1000 may be coupled to various radios, andtransmitters 1059 for manual control of the UAV, and for wireless orwired data transmission to and from the UAV primary processing system1000, and optionally the UAV secondary processing system 1002. The UAVmay use one or more communications subsystems, such as a wirelesscommunication or wired subsystem, to facilitate communication to andfrom the UAV. Wireless communication subsystems may include radiotransceivers, and infrared, optical ultrasonic, electromagnetic devices.Wired communication systems may include ports such as Ethernet, USBports, serial ports, or other types of port to establish a wiredconnection to the UAV with other devices, such as a ground controlsystem, flight planning system, or other devices, for example a mobilephone, tablet, personal computer, display monitor, other network-enableddevices. The UAV may use a light-weight tethered wire to a groundcontrol station for communication with the UAV. The tethered wire may beremoveably affixed to the UAV, for example via a magnetic coupler.

Flight data logs may be generated by reading various information fromthe UAV sensors and operating system and storing the information innon-volatile memory. The data logs may include a combination of variousdata, such as time, altitude, heading, ambient temperature, processortemperatures, pressure, battery level, fuel level, absolute or relativeposition, GPS coordinates, pitch, roll, yaw, ground speed, humiditylevel, velocity, acceleration, contingency information. This foregoingis not meant to be limiting, and other data may be captured and storedin the flight data logs. The flight data logs may be stored on aremovable media and the media installed onto the ground control system.Alternatively, the data logs may be wirelessly transmitted to the groundcontrol system or to the flight planning system.

Modules, programs or instructions for performing flight operations,contingency maneuvers, and other functions may be performed with theoperating system. In some implementations, the operating system 120 canbe a real time operating system (RTOS), UNIX, LINUX, OS X, WINDOWS,ANDROID or other operating system. Additionally, other software modulesand applications may run on the operating system, such as a flightcontrol module 1022, contingency module 1024, application module 1026,and database module 1028. Typically flight critical functions will beperformed using the UAV processing system 1000. Operating system 1020may include instructions for handling basic system services and forperforming hardware dependent tasks.

In addition to the UAV primary processing system 1000, a secondaryprocessing system 1002 may be used to run another operating system toperform other functions. A UAV secondary processing system 1002 can be asystem of one or more computers, or software executing on a system ofone or more computers, which is in communication with, or maintains, oneor more databases. The UAV secondary processing system 1002 can be asystem of one or more processors 1094, graphics processors 1092, I/Osubsystem 1094 logic circuits, analog circuits, associated volatileand/or non-volatile memory, associated input/output data ports, powerports, etc., and/or one or more software processing executing one ormore processors or computers. Memory 1070 may include non-volatilememory, such as one or more magnetic disk storage devices, solid statehard drives, flash memory. Other volatile memory such a RAM, DRAM, SRAMmay be used for storage of data while the UAV is operational.

Ideally modules, applications and other functions running on thesecondary processing system 1002 will be non-critical functions innature. That is, if the function fails, the UAV will still be able tosafely operate. In some implementations, the operating system 1072 canbe based on real time operating system (RTOS), UNIX, LINUX, OS X,WINDOWS, ANDROID or other operating system. Additionally, other softwaremodules and applications may run on the operating system 1072, such asan application module 1074, database module 1076. Operating system 1002may include instructions for handling basic system services and forperforming hardware dependent tasks.

Also, controllers 1046 may be used to interact and operate a payloaddevice 1048, and other devices such as photographic camera 1049, videocamera, infra-red camera, multispectral camera, stereo camera pair,Lidar, radio transceiver, sonar, laser ranger, altimeter, TCAS (trafficcollision avoidance system), ADS-B (Automatic dependentsurveillance—broadcast) transponder. Optionally, the secondaryprocessing system 1002 may have coupled controllers to control payloaddevices.

Each of the processes, methods, instructions, applications andalgorithms described in the preceding sections may be embodied in, andfully or partially automated by, code modules executed by one or morecomputer systems or computer processors comprising computer hardware.The code modules (or “engines”) may be stored on any type ofnon-transitory computer-readable medium or computer storage device, suchas hard drives, solid state memory, optical disc, and/or the like. Thesystems and modules may also be transmitted as generated data signals(for example, as part of a carrier wave or other analog or digitalpropagated signal) on a variety of computer-readable transmissionmediums, including wireless-based and wired/cable-based mediums, and maytake a variety of forms (for example, as part of a single or multiplexedanalog signal, or as multiple discrete digital packets or frames). Theprocesses and algorithms may be implemented partially or wholly inapplication-specific circuitry. The results of the disclosed processesand process steps may be stored, persistently or otherwise, in any typeof non-transitory computer storage such as, for example, volatile ornon-volatile storage.

User interfaces described herein are optionally presented (and userinstructions may be received) via a user computing device using abrowser, other network resource viewer, a dedicated application, orotherwise. Various features described or illustrated as being present indifferent embodiments or user interfaces may be combined into the sameembodiment or user interface. Commands and information received from theuser may be stored and acted on by the various systems disclosed hereinusing the processes disclosed herein. While the disclosure may referenceto a user hovering over, pointing at, or clicking on a particular item,other techniques may be used to detect an item of user interest. Forexample, the user may touch the item via a touch screen, or otherwiseindicate an interest. The user interfaces described herein may bepresented on a user terminal, such as a laptop computer, desktopcomputer, tablet computer, smart phone, virtual reality headset,augmented reality headset, or other terminal type. The user terminalsmay be associated with user input devices, such as touch screens,microphones, touch pads, keyboards, mice, styluses, cameras, etc. Whilethe foregoing discussion and figures may illustrate various types ofmenus, other types of menus may be used. For example, menus may beprovided via a drop down menu, a tool bar, a pop up menu, interactivevoice response system, or otherwise.

In general, the terms “engine” and “module”, as used herein, refer tologic embodied in hardware or firmware, or to a collection of softwareinstructions, possibly having entry and exit points, written in aprogramming language, such as, for example, Java, Lua, C or C++. Asoftware module may be compiled and linked into an executable program,installed in a dynamic link library, or may be written in an interpretedprogramming language such as, for example, BASIC, Perl, or Python. Itwill be appreciated that software modules may be callable from othermodules or from themselves, and/or may be invoked in response todetected events or interrupts. Software modules configured for executionon computing devices may be provided on a computer readable medium, suchas a compact disc, digital video disc, flash drive, or any othertangible medium. Such software code may be stored, partially or fully,on a memory device of the executing computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules may be comprised of connectedlogic units, such as gates and flip-flops, and/or may be comprised ofprogrammable units, such as programmable gate arrays or processors. Themodules described herein are preferably implemented as software modules,but may be represented in hardware or firmware. Generally, the modulesdescribed herein refer to logical modules that may be combined withother modules or divided into sub-modules despite their physicalorganization or storage. Electronic data sources can include databases,volatile/non-volatile memory, and any memory system or subsystem thatmaintains information.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and subcombinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The example systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “for example,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without author input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list. Conjunctivelanguage such as the phrase “at least one of X, Y and Z,” unlessspecifically stated otherwise, is otherwise understood with the contextas used in general to convey that an item, term, etc. may be either X, Yor Z. Thus, such conjunctive language is not generally intended to implythat certain embodiments require at least one of X, at least one of Yand at least one of Z to each be present.

The term “a” as used herein should be given an inclusive rather thanexclusive interpretation. For example, unless specifically noted, theterm “a” should not be understood to mean “exactly one” or “one and onlyone”; instead, the term “a” means “one or more” or “at least one,”whether used in the claims or elsewhere in the specification andregardless of uses of quantifiers such as “at least one,” “one or more,”or “a plurality” elsewhere in the claims or specification.

The term “comprising” as used herein should be given an inclusive ratherthan exclusive interpretation. For example, a general purpose computercomprising one or more processors should not be interpreted as excludingother computer components, and may possibly include such components asmemory, input/output devices, and/or network interfaces, among others.

While certain example embodiments have been described, these embodimentshave been presented by way of example only, and are not intended tolimit the scope of the disclosure. Nothing in the description isintended to imply that any particular element, feature, characteristic,step, module, or block is necessary or indispensable. The novel methodsand systems described herein may be embodied in a variety of otherforms; furthermore, various omissions, substitutions, and changes in theform of the methods and systems described herein may be made withoutdeparting from the spirit of the inventions disclosed herein. Theaccompanying claims and their equivalents are intended to cover suchforms or modifications as would fall within the scope and spirit ofcertain of the inventions disclosed herein.

Any process descriptions, elements, or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved, as would be understood by those skilled in the art.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of the disclosure. The foregoing description details certainembodiments of the invention. It will be appreciated, however, that nomatter how detailed the foregoing appears in text, the invention can bepracticed in many ways. As is also stated above, it should be noted thatthe use of particular terminology when describing certain features oraspects of the invention should not be taken to imply that theterminology is being re-defined herein to be restricted to including anyspecific characteristics of the features or aspects of the inventionwith which that terminology is associated.

What is claimed is:
 1. A method comprising: determining an upcomingweather event and one or more areas expected to be affected by theupcoming weather event based on outputs from a plurality of sensorsmonitoring features of weather events; determining that a likelihood ofdamage associated with the upcoming weather event in the one or moreareas is greater than a first threshold using machine learning modelstrained on historical weather event information; based on the likelihoodof damage being greater than the first threshold, determining thatseverity information associated with the upcoming weather event withinthe one or more areas is greater than a second threshold; determining,prior to the upcoming weather event, properties to be inspected withinthe one or more areas by one or more unmanned aerial vehicles (UAVs)based on the severity information being greater than the secondthreshold; automatically initiating, subsequent to the upcoming weatherevent, an initial UAV inspection of the properties determined based onthe severity information being greater than the second threshold toidentify a subset of the properties for which to perform detailed UAVinspections, wherein the initial UAV inspection is performed using afirst UAV to navigate about the one or more areas according to a firstflight pattern to obtain initial sensor information; analyzing theinitial sensor information from the initial UAV inspection performedusing the first UAV to identify the subset of the properties byidentifying damage to particular properties indicated by the initialsensor information; and automatically initiating, subsequent to theinitial UAV inspection, based on the initial sensor information obtainedby the first UAV, the detailed UAV inspections of the subset of theproperties, wherein the detailed UAV inspections are performed using asecond UAV to navigate the subset of the properties according to asecond flight pattern to obtain detailed sensor information of theparticular properties.
 2. The method of claim 1, wherein determining theupcoming weather event and the one or more areas expected to be affectedby the upcoming weather event based on the outputs from the plurality ofsensors monitoring features of weather events comprises: parsing one ormore content items maintained by a governmental entity associated withweather events.
 3. The method of claim 1, wherein determining that theseverity information associated with the upcoming weather event withinthe one or more areas is greater than the second threshold comprises:identifying a type of the upcoming weather event along with the severityinformation associated with the upcoming weather event.
 4. The method ofclaim 3, wherein the severity information comprises one or more of asize associated with hail, a speed of wind, a ranking of a tornado, anamount of snow, or a magnitude of an earthquake.
 5. The method of claim1, wherein the initial UAV inspection utilizes a fixed-wing UAV for thefirst UAV, and the detailed UAV inspections utilize a multi-rotor UAVfor the second UAV.
 6. The method of claim 1, wherein the initial sensorinformation analysis is based on computer vision techniques that aretrained to identify features associated with damage.
 7. The method ofclaim 1, further comprising: generating a user interface data thatincludes the initial sensor information; and receiving user inputspecifying assignments of the properties that are to be inspected inmore detail.
 8. The method of claim 1, wherein determining theproperties to be inspected within the one or more areas by the firstUAVs based on the severity information being greater than the secondthreshold comprises: accessing, via one or more networks, informationobtained from hardware comprising sensors, the hardware being installedon properties and monitoring information indicative of weather events;and determining, based on the accessed information obtained fromhardware, that the particular properties are to be inspected in moredetail.
 9. The method of claim 8, wherein the particular properties tobe inspected are determined based on reductions in efficiencies of solarcells installed on the particular properties, the reductions indicatingpotential damage to the solar cells.
 10. The method of claim 1, furthercomprising: generating jobs for the detailed UAV inspections of thesubset of the properties, wherein a job of the jobs specifies one orboth of a location associated with a property to be inspected, or ageofence boundary representing a geospatial boundary in which the secondUAVs performing the detailed UAV inspections is limited.
 11. Anon-transitory computer storage medium storing instructions operable tocause one or more processors to: determine an upcoming weather event andone or more areas expected to be affected by the weather event based onoutputs from a plurality of sensors monitoring features of upcomingweather events; determine that a likelihood of damage associated withthe upcoming weather event in the one or more areas is greater than afirst threshold using machine learning models trained on historicalweather event information; based on the likelihood of damage beinggreater than the first threshold, determine, prior to the upcomingweather event, properties to be inspected within the one or more areasby one or more unmanned aerial vehicles (UAVs) based on severityinformation associated with the upcoming weather event within the one ormore areas being greater than a second threshold; automaticallyinitiate, subsequent to the upcoming weather event, an initial UAVinspection of the properties determined based on the severityinformation being greater than the second threshold to identify a subsetof the properties for which to perform detailed UAV inspections, whereinthe initial UAV inspection is performed using a first UAV to navigateabout the one or more areas according to a first flight pattern toobtain initial sensor information; analyzing the initial sensorinformation from the initial UAV inspection performed using the firstUAV to identify the subset of the properties by identifying damage toparticular properties indicated by the initial sensor information; andautomatically initiate, subsequent to the initial UAV inspection, basedon the initial sensor information obtained by the first UAV, thedetailed UAV inspections of the subset of the properties, wherein thedetailed UAV inspections are performed using a second UAV to navigatethe subset of the properties according to a second flight pattern toobtain detailed sensor information of the particular properties.
 12. Thenon-transitory computer storage medium of claim 11, wherein, todetermine the upcoming weather event and the one or more areas expectedto be affected by the upcoming weather event based on the outputs fromthe plurality of sensors monitoring features of weather events, theinstructions, when executed by the one or more processors, cause the oneor more processors to: parse one or more content items maintained by agovernmental entity associated with weather events.
 13. Thenon-transitory computer storage medium of claim 11, wherein theinstructions are operable to cause the one or more processors to:determine the severity information associated with the weather event.14. The non-transitory computer storage medium of claim 13, wherein theseverity information comprises one or more of a size associated withhail, a speed of wind, a ranking of a tornado, an amount of snow, or amagnitude of an earthquake.
 15. The non-transitory computer storagemedium of claim 11, wherein the initial UAV inspection utilizes afixed-wing UAV for the first UAV, and the detailed UAV inspectionsutilize a multi-rotor UAV for the second UAV.
 16. The non-transitorycomputer storage medium of claim 11, wherein the initial sensorinformation analysis is based on computer vision techniques that aretrained to identify features associated with damage.
 17. Thenon-transitory computer storage medium of claim 11, wherein theinstructions are operable to cause the one or more processors to:generate a user interface that includes the initial sensor information;and receive user input specifying assignments of the properties that areto be inspected in more detail.
 18. The non-transitory computer storagemedium of claim 11, wherein, to determine the properties to be inspectedwithin the one or more areas by the first UAV based on the severityinformation being greater than the second threshold, the instructions,when executed by the one or more processors, cause the one or moreprocessors to: access, via one or more networks, information obtainedfrom hardware comprising sensors, the hardware being installed onproperties and monitoring information indicative of weather events; anddetermine, based on the accessed information obtained from hardware,that the particular properties are to be inspected in more detail. 19.The non-transitory computer storage medium of claim 18, wherein theparticular properties to be inspected are determined based on reductionsin efficiencies of solar cells installed on the particular properties,the reductions indicating potential damage to the solar cells.
 20. Thenon-transitory computer storage medium of claim 11, wherein theinstructions, when executed by the one or more processors, cause the oneor more processors to: generate jobs for the detailed UAV inspections ofthe subset of the properties, wherein a job of the jobs specifies one orboth of a location associated with a property to be inspected or ageofence boundary representing a geospatial boundary in which a UAVperforming an inspection is limited.
 21. A system comprising: one ormore computer systems including a job determination engine and a weatherevent determination engine, wherein the weather event determinationengine is configured to determine an upcoming weather event and one ormore areas expected to be affected by the upcoming weather event basedon outputs from a plurality of sensors monitoring features of weatherevents, determine that a likelihood of damage associated with theupcoming weather event in the one or more areas is greater than a firstthreshold using machine learning models trained on historical weatherevent information, determine that severity information associated withthe upcoming weather event within the one or more areas is greater thana second threshold based on the likelihood of damage being greater thanthe first threshold, and determine properties to be inspected within theone or more areas by one or more unmanned aerial vehicles (UAVs) basedon the severity information being greater than the second thresholdprior to the upcoming weather event, and wherein the job determinationengine is configured to generate one or more flight plans for at leastone of the UAVs to navigate to automatically initiate detailed UAVinspections of a subset of the properties identified based on an initialUAV inspection of the properties determined based on the severityinformation being greater than the second threshold, wherein the initialUAV inspection is performed using a first UAV to navigate about the oneor more areas according to a first flight pattern to obtain initialsensor information, wherein the initial sensor information from theinitial UAV inspection performed using the first UAV is analyzed toidentify the subset of the properties by identifying damage toparticular properties indicated by the initial sensor information, andwherein the detailed UAV inspections are automatically initiated, basedon the initial sensor information obtained by the first UAV, using asecond UAV to navigate the subset of the properties according to asecond flight pattern to obtain detailed sensor information of theparticular properties.
 22. The system of claim 21, wherein, to determinethe upcoming weather event and the one or more areas expected to beaffected by the upcoming weather event based on the outputs from theplurality of sensors monitoring features of weather events, the weatherevent determination engine is configured to: parse one or more contentitems maintained by a governmental entity associated with weatherevents.
 23. The system of claim 21, wherein, to determine that theseverity information associated with the upcoming weather event withinthe one or more areas is greater than the second threshold, the weatherevent determination engine is configured to: identify a type of theupcoming weather event along with the severity information associatedwith the upcoming weather event.
 24. The system of claim 23, wherein theseverity information comprises one or more of a size associated withhail, a speed of wind, a ranking of a tornado, an amount of snow, or amagnitude of an earthquake.
 25. The system of claim 21, wherein theinitial UAV inspection utilizes a fixed-wing UAV for the first UAV, andthe detailed UAV inspections utilize a multi-rotor UAV for the secondUAV.
 26. The system of claim 21, wherein the initial sensor informationanalysis is based on computer vision techniques that are trained toidentify features associated with damage.
 27. The system of claim 21,wherein the job determination engine is further configured to: generatea user interface that includes the initial sensor information, andreceive user input specifying assignments of the properties that are tobe inspected in more detail.
 28. The system of claim 21, wherein, todetermine the properties to be inspected within the one or more areas bythe first UAVs based on the severity information being greater than thesecond threshold, the weather event determination engine is configuredto: access, via one or more networks, information obtained from hardwarecomprising sensors, the hardware being installed on properties andmonitoring information indicative of weather events; and determine,based on the accessed information obtained from hardware, that theparticular properties are to be inspected in more detail.
 29. The systemof claim 28, wherein the particular properties to be inspected aredetermined based on reductions in efficiencies of solar cells installedon the particular properties, the reductions indicating potential damageto the solar cells.
 30. The system of claim 21, wherein the jobdetermination engine is further configured to: generate jobs for thedetailed UAV inspections of the subset of the properties, wherein a jobof the jobs specifies one or both of a location associated with aproperty to be inspected or a geofence boundary representing ageospatial boundary in which the second UAV performing the detailed UAVinspections is limited.